Targeting SNAI1-Mediated Colorectal Cancer Chemoresistance and Stemness by Sphingosine Kinase 2 Inhibition
Bibliographic record
Abstract
Background: Epithelial-to-mesenchymal transition (EMT), cancer stem cells (CSCs), and colorectal cancer (CRC) therapy resistance are closely associated. Prior reports have demonstrated that sphingosine-1-phosphate (S1P) supports stem cells and maintains the CSC phenotype. We hypothesized that the EMT inducer SNAI1 drives S1P signaling to amplify CSC self-renewal capacity and chemoresistance. Methods: CRC cell lines with or without ectopic expression of SNAI1 were used to study the role of S1P signaling as mediators of cancer stemness and 5-fluorouracil (5FU) chemoresistance. The therapeutic ability of sphingosine kinase 2 (SPHK2) was assessed using siRNA and ABC294640, a SPHK2 inhibitor. CSCs were isolated from patient-derived xenografts (PDXs) and assessed for SPHK2 and SNAI1 expression. Results: self-renewal and sensitized cells to 5FU. CSCs isolated from CRC PDXs express increased SPHK2 relative to the non-CSC population. Combination ABC294640/5FU therapy significantly inhibited tumor growth in mice and enhanced 5FU response in therapy-resistant CRC patient-derived tumor organoids (PDTOs). Conclusions: SNAI1/SPHK2 signaling mediates cancer stemness and 5FU resistance, implicating S1P as a therapeutic target for CRC. The S1P inhibitor ABC294640 holds potential as a therapeutic agent to target CSCs in therapy refractory CRC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".